As the awareness of energy saving and dioxide emission reduction becomes popular in recent years, some users choose to install power meters on some electric devices having a high power consumption in order to reduce the power consumption. The power meters periodically measures the power consumption and reports the power consumption back to a power monitoring device. The power monitoring device stores power specification information (e.g., an operating status, a maximum power value, a minimum power value and a power value distribution of each of the electric devices), and generates a piece of user behavior feature information (e.g., a using status, a using frequency, a starting time, a stopping time, a continuous use duration, a number of use times per day, a number of use times per week, a number of use times per quarter, a number of use times per year, power consumption each time, power consumption per day, power consumption per week, power consumption per month, and so on) according to the power consumption measured. In this way, the user can be informed of the using conditions and the power consumption per day, per week or per month of the electric devices according to the power specification information and the user behavior feature information.
However, it requires a very large storage space to store the power specification information and all the user behavior feature information. Furthermore, it may take the user considerable time to find a piece of desired or key power consumption information from the massive user behavior feature information.
Accordingly, an urgent need exists in the art to provide a solution capable of retrieving key user behavior feature information from all pieces of user behavior feature information so that only the key user behavior feature information is stored to reduce the information storage amount and increase the retrieving efficiency.